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Regression

Models

LearningHorse.Regression.LinearRegression — Type
LinearRegression()

Classic regression model. This struct has no parameter. If you want to use polynomial model, use Regression.make_design_matrix().

see also: make_design_matrix

Example

julia> x = [
    16.862463771320925 68.10823385851712
    15.382965696961577 65.4313485700859
    8.916228406218375 53.92034559524475
    10.560285659132695 59.17305391117168
    12.142253214135884 62.28708207525656
    5.362107221163482 43.604947901567414
    13.893239446341777 62.44348617377496
    11.871357065173395 60.28433066289655
    29.83792267802442 69.22281924803998
    21.327107214235483 70.15810991597944
    23.852372696012498 69.81780163668844
    26.269031430914108 67.61037566099782
    22.78907104644012 67.78105545358633
    26.73342178134947 68.59263965946904
    9.107259141706415 56.565383817343495
    29.38551885863976 68.1005579469209
    7.935966787763017 53.76264777936664
    29.01677894379809 68.69484161138638
    6.839609488194577 49.69794758177567
    13.95215840314148 62.058116579899085]; #These data are also used to explanations of other functions.

julia> t = [169.80980778351542, 167.9081124078835, 152.30845618985222, 160.3110300206261, 161.96826472170756, 136.02842285615077, 163.98131131382686, 160.117817321485, 172.22758529098235, 172.21342437006865, 171.8939175591617, 169.83018083884602, 171.3878062674257, 170.52487535026015, 156.40282783981309, 170.6488327896672, 151.69267899906185, 172.32478221316322, 145.14365314788827, 163.79383292080666];

julia> model = LinearRegression()
LinearRegression(Float64[])

julia> fit!(model, x, t)
3-element Vector{Float64}:
 -0.04772448076255398
  1.395963968616736
 76.7817095600793

julia> model(x)
20-element Vector{Float64}:
 171.05359766482795
 167.38737053144575
 151.62704681535598
 158.88117658330424
 163.15274911747872
 137.3967419011542
 163.28751869479999
 160.3699086857777
 171.99027166957023
 173.70207799107243
 173.10650291105486
 169.9096820022986
 170.31402414534642
 171.25872436348817
 155.31030802635905
 170.44522606721017
 151.45368882321284
 171.29242257091374
 145.83183688699864
 162.74674475052848
source
LearningHorse.Regression.Lasso — Type
Lasso(; alpha = 0.1, tol = 1e-4, mi = 1e+8)

Lasso Regression structure. eEach parameters are as follows:

  • alpha : leaarning rate.
  • tol : Allowable error.
  • mi : Maximum number of learning.

Example

julia> model = Lasso()
Lasso(Float64[], 0.1, 0.0001, 100000000)

julia> fit!(model, x, t)
3-element Vector{Float64}:
   0.0
   0.5022766549841176
 154.43624186616267

julia> model(x)
20-element Vector{Float64}:
 188.64541774549468
 187.30088075704523
 181.5191726873298
 184.15748544986084
 185.72158909964372
 176.33798923891868
 185.80014722707335
 184.71565381947883
 189.20524796663838
 189.67502263476888
 189.50409373058318
 188.39535519538825
 188.481083670683
 188.88872347085172
 182.8477136378307
 188.64156231429416
 181.43996475587224
 188.9400571253936
 179.39836073711297
 185.6065850765288
source
LearningHorse.Regression.Ridge — Type
Ridge(alpha = 0.1)

Ridge Regression. alpha is the value multiplied by regularization term.

Example

julia> model = Ridge()
Ridge(Float64[], 0.1)

julia> fit!(model, x, t)
3-element Vector{Float64}:
 -0.5635468573581848
  2.1185952951687614
 40.334109796666425

julia> model(x)
20-element Vector{Float64}:
 175.12510514593038
 170.28763505842625
 149.54478779081344
 159.7466476176333
 165.4525001910219
 129.6935485937018
 164.79709438985097
 161.3621431448216
 170.17418141858434
 176.95192713546982
 174.8078461898064
 168.76930346791391
 171.09202561187362
 170.58861763111338
 155.04089855305028
 168.05151465675456
 149.76311329450505
 169.5183634524783
 141.7695072903308
 163.94744858842117
source

Other